CN117911060B - Price comparison system for bid commodity for electronic commerce transaction - Google Patents
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Abstract
The invention discloses a price comparison system of a bid commodity for electronic commerce transaction, which comprises an information monitoring unit, a preliminary analysis unit, a depth comparison unit and a price prediction unit, wherein a price preprocessing model is established through the preliminary analysis unit, the amplitude and frequency abnormal change of a price curve is analyzed for any commodity bid, the price floating condition of the commodity bid is further integrated and acquired, the depth comparison unit is established to comprehensively analyze the whole price floating condition of all the bids, the price competitiveness of the bid is further analyzed, the risk degree of the current commodity and the bid in price comparison competition is acquired, the price competition risk degree of the current commodity is judged through the price prediction unit for price competition monitoring and early warning, and therefore the commodity price is regulated and controlled in time, accurate, comprehensive and timely price monitoring management is realized, and market activity is kept.
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to a bid commodity price comparison system for electronic commerce transaction.
Background
In the electronic commerce transaction process, price competition may exist between any commodity and the bidding product, under the condition of the same quality performance, a consumer tends to select the commodity with lower price and higher cost performance, and as a certain price psychological expectation exists generally for the consumer, namely the price of the commodity cannot be suddenly reduced after the commodity is purchased, the commodity with more stable price is more prone to be purchased, so factors such as higher cost performance, more stable price and the like form the price competitiveness of the commodity together, and the price competition is very necessary in the price comparison monitoring process of the bidding product commodity;
However, the existing electronic commerce has certain defects in the price comparison process of the bid commodity, and the frequency and the amplitude of price floating of the bid commodity have certain influence on the price competitiveness of the bid commodity, but the direct comparison of the price data at the current time point has the defects of low reference value and unsophisticated data processing, so that the problems of incomplete price comparison of the commodity, insufficient deep risk analysis of the price competitiveness, high delay degree of price management regulation and control and the like can be caused;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims at: the method solves the problems that data processing is not fine, price comparison of the commodity is incomplete, price competitiveness risk analysis is not deep enough, delay degree of price management regulation is high and the like in the commodity price comparison process, analyzes abnormal changes of amplitude and frequency of a price curve aiming at any commodity, further integrates and obtains price floating conditions of the commodity, comprehensively analyzes the whole price floating conditions of all the commodities, further deeply analyzes price competitiveness of the commodity, and accordingly obtains the risk degree of the current commodity and the commodity in price comparison competition, judges the price competition risk degree of the current commodity through monitoring and early warning analysis, timely regulates and controls commodity price, and achieves accurate, comprehensive and timely price monitoring management, so that market activity is maintained.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The bid commodity price comparison system for electronic commerce transaction comprises an information monitoring unit, a preliminary analysis unit, a depth comparison unit and a price prediction unit, wherein the information monitoring unit, the preliminary analysis unit, the depth comparison unit and the price prediction unit are in signal connection;
The information monitoring unit is used for collecting price information: setting an information acquisition period as Tc, and carrying out timing acquisition on price information, wherein the price information comprises commodity price and bid price, and the number of the acquired bid items is preset to be N0;
the preliminary analysis unit is used for establishing a price preprocessing model: firstly, establishing a dynamic curve change chart of commodity prices and N0 bid prices, and then carrying out price floating analysis on a single curve to obtain price floating evaluation coefficients corresponding to the curve;
The depth comparison unit is used for establishing a price comparison model: firstly, synthesizing the price floating evaluation coefficients of N0 competitive products to obtain the overall price evaluation index of the competitive products, and then comparing the price floating evaluation coefficient of the current commodity with the overall floating evaluation index of the competitive products to obtain the overall competition evaluation index;
the price prediction unit is used for price competitiveness monitoring and early warning: and establishing a dynamic graph of the comprehensive competition evaluation index, performing curve anomaly monitoring analysis, judging the price competition risk degree of the current commodity, and generating an early warning prompt signal and a price comparison evaluation report of the competitive commodity so as to regulate and control the commodity price in time.
Further, the specific process of establishing the price preprocessing model is as follows:
A1: firstly, building a dynamic curve change graph of commodity prices and N0 bid prices:
Marking the price of the current commodity as P0, marking any bid as I, and marking the price of the bid as Pi, wherein I is more than 0 and less than or equal to N0, and I is a natural number;
constructing a dynamic curve graph S0 of the commodity price P0-information acquisition period Tc;
Constructing a dynamic curve graph Si of a bid price Pi-information acquisition period Tc;
a2: and then establishing a curve floating analysis model, and carrying out price floating analysis on a single curve:
A2-1: marking an input curve as Sj, carrying out floating analysis on the curve Sj, presetting a floating interval of a ordinate of the curve as [ J1, J2], extracting a curve segment of the ordinate exceeding the floating interval [ J1, J2] in the curve Sj, and marking the curve segment as an abnormal segment of the floating curve, wherein the curve segment of the ordinate higher than J2 is marked as a high-amplitude abnormal segment, and the curve segment of the ordinate lower than J1 is marked as a low-amplitude abnormal segment;
The method comprises the steps of presetting the number of high-amplitude abnormal fragments and low-amplitude abnormal fragments to be n1 and n2 respectively, and respectively analyzing the high-amplitude abnormal fragments and the low-amplitude abnormal fragments to obtain a high-amplitude abnormal coefficient GF of a high-amplitude abnormal fragment Cg and a low-amplitude abnormal coefficient DF of a low-amplitude abnormal fragment Cd;
a2-2: integrating and marking high-amplitude peak points and low-amplitude peak points as abnormal peak points, integrating and marking high-amplitude valley points and low-amplitude valley points as abnormal valley points, setting a time section t of an abscissa in a curve Sj, integrating and accumulating the abnormal peak points and the abnormal valley points in the time section t to obtain the total number Zs of the abnormal points, setting a threshold Z0 of the total number of the abnormal points, marking the total number Zs of the abnormal points as high-frequency fluctuation segments when the total number of the abnormal points exceeds the threshold Z0, and extracting all the high-frequency fluctuation segments in the curve Sj;
N3 preset high-frequency fluctuation segments are provided, slope changes of the high-frequency fluctuation segments are analyzed, and high-frequency abnormal coefficients GP of the high-frequency fluctuation segments Cp are obtained;
A2-3: integrating n1 high-amplitude abnormal coefficients and n2 low-amplitude abnormal coefficients to generate amplitude abnormal coefficients, integrating high-frequency abnormal coefficients of n3 high-frequency fluctuation segments to generate frequency abnormal coefficients, and establishing a formula to obtain a price floating evaluation coefficient FD;
A3: substituting the dynamic curve S0 of the commodity price P0 and the dynamic curves of the N0 bid prices into a curve floating analysis model respectively to obtain price floating evaluation coefficients corresponding to the curves:
marking the price floating evaluation coefficient of the dynamic curve S0 as FD0;
the price-floating assessment coefficient of the dynamic curve Si is labeled FDi.
Further, the specific process of analyzing the high-amplitude abnormal segment and the low-amplitude abnormal segment is as follows:
a2-11: analyzing the high-amplitude abnormal fragments, and marking any high-amplitude abnormal fragment as Cg:
For the high-amplitude abnormal segment Cg, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kg1 and a right slope Kg2, wherein the left slope Kg1 is larger than 0, and the right slope Kg2 is smaller than 0;
all points with the slope of 0 are obtained, and marked as peaks Gu Biaodian of the high-amplitude abnormal segment Cg, the total number Mc0 of peaks Gu Biaodian of the high-amplitude abnormal segment Cg is obtained, any one of the peaks Gu Biaodian is marked as c0, and the high-amplitude peak point and the high-amplitude valley point are determined to be obtained:
Acquiring a high-amplitude fluctuation slope BKc through the slope between the adjacent high-amplitude peak point Fc0 and the high-amplitude valley point Gc0, wherein if the high-amplitude fluctuation slope BKc is larger than 0, the high-amplitude fluctuation slope BKc is marked as a high-amplitude rising slope BKc +; if the high-amplitude fluctuation slope BKc is smaller than 0, marking the high-amplitude fluctuation slope BKc as a high-amplitude falling slope BKc -;
the conversion coefficient of the high-amplitude abnormal segment Cg is given and a formula is established by combining the left slope Kg1, the right slope Kg2, the high-amplitude rising slope BKc +, the high-amplitude falling slope BKc - and the total number Mc0 of peak-valley punctuation points, so that the high-amplitude abnormal coefficient GF of the high-amplitude abnormal segment Cg is obtained;
A2-12: analyzing the low-amplitude abnormal fragments, and marking any low-amplitude abnormal fragment as Cd:
For the low-amplitude abnormal segment Cd, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kd1 and a right slope Kd2, wherein the left slope Kd1 is smaller than 0, and the right slope Kd2 is larger than 0;
And acquiring all points with the slope of 0, marking the points as peaks Gu Biaodian of the low-amplitude abnormal segment Cd, acquiring the total number Mc1 of peaks Gu Biaodian of the low-amplitude abnormal segment Cd, marking any one of the peaks Gu Biaodian as d0, and judging to acquire a low-amplitude peak point and a low-amplitude valley point:
Obtaining a low amplitude fluctuation slope BKd through the slope between the adjacent low amplitude peak point Fd0 and low amplitude valley point Gd0, wherein if the low amplitude fluctuation slope BKd is larger than 0, the low amplitude fluctuation slope is marked as a low amplitude rising slope BKd +; if the low amplitude fluctuation slope BKd is less than 0, it is marked as a low amplitude falling slope BKd -;
The conversion coefficient of the low-amplitude abnormal segment Cd is given and a formula is established by combining the left slope Kd1, the right slope Kd2, the low-amplitude rising slope BKd +, the low-amplitude falling slope BKd - and the total number Mc1 of peak-valley punctuations, so that the low-amplitude abnormal coefficient DF of the low-amplitude abnormal segment Cd is obtained.
Further, the specific process of analyzing the high-frequency fluctuation segment is as follows:
Any one of the high frequency fluctuation segments is marked Cp: all points and slopes of a high-frequency fluctuation segment Cp are obtained, n4 points are preset in the high-frequency fluctuation segment Cp, any point of the high-frequency fluctuation segment Cp is marked as p, the point adjacent to the point p is marked as q, the slope of the point p is marked as Kp, the slope of the point q is marked as Kq, the slope change value Kpq between the point p and the point q is obtained, and as the high-frequency fluctuation segment Cp has n4 points, slope change values are obtained between the two adjacent points, [ (n 4) -1] slope change values are obtained in sequence;
And respectively endowing corresponding weight factor coefficients for the total number Zs of the abnormal points and [ (n 4) -1] slope change values, and establishing a formula to obtain a high-frequency abnormal coefficient GP of the high-frequency fluctuation segment Cp.
Further, the specific process of obtaining the overall price evaluation index of the bid product is as follows:
Firstly, synthesizing price floating evaluation coefficients of N0 bid products, endowing conversion coefficients and establishing a formula to obtain an overall price floating index Zjp of the bid products;
reconstructing a dynamic curve Sz of the overall price floating index Zjp-information acquisition period Tc of the bid, and analyzing the curve Sz:
the ordinate average value of the dynamic curve Sz is obtained and marked as the bidding average price floating index Further, the maximum value Zmax of the overall price floating index Zjp of the bid is obtained through sequencing, and the current average price of the bid is obtained by averaging the current prices of N0 bids
Current average price of re-bidAverage price float indexAnd the maximum value Zmax of the overall price float index Zjp, combine the average price float indexThe maximum value Zmax of the overall price floating index Zjp is combined to generate the price floating coefficient of the bid product;
for the current average price of the bid And respectively giving corresponding weight factor coefficients to the price floating coefficients, and establishing a formula to obtain the overall price evaluation index Jjp of the bid.
Further, the specific process of obtaining the comprehensive competition assessment index is as follows:
The price P0 of the current commodity is combined with the price floating evaluation coefficient FD0 of the current commodity by combining the price P0 of the current commodity, the price floating evaluation coefficient FD0 of the current commodity and the overall price evaluation index Jjp of the bid product, so that the price evaluation coefficient of the current commodity is generated;
and respectively endowing the price evaluation coefficient of the current commodity and the overall price evaluation index Jjp of the bid with corresponding proportion coefficients, and establishing a formula to obtain the comprehensive competition evaluation index JZ of the current commodity.
Further, the specific process of dynamic curve anomaly monitoring analysis of the comprehensive competition assessment index is as follows:
with the information acquisition period Tc as an abscissa and the comprehensive competition evaluation index JZ of the current commodity as an ordinate, establishing a dynamic graph Sjz of the comprehensive competition evaluation index JZ-information acquisition period Tc of the current commodity, and carrying out anomaly monitoring analysis on the dynamic graph Sjz:
All points on the dynamic curve Sjz are acquired firstly, N1 points are preset on the dynamic curve Sjz, any point is marked as W (Xw, yw), the point closest to the point W is marked as V (Xv, yv), and then the slope Kw of the point W is acquired; the slope of N1 points is determined and averaged to mark it as the average increment rate of the dynamic curve Sjz
Then obtaining the average index of comprehensive competition by obtaining the average value of the ordinate of N1 points of the dynamic curve SjzFurther solving standard deviation, and obtaining a fluctuation coefficient sigma of the dynamic curve Sjz;
the ordinate of the end point of the dynamic curve Sjz is obtained and compared with the average increment of the dynamic curve Sjz Average index of comprehensive competitionCombining the fluctuation coefficient sigma, and establishing a formula to obtain a price competition risk index FX;
setting a risk interval of price competition risk index FX, dividing the competition risk degree of the current commodity, and generating a corresponding early warning prompt signal and a price comparison evaluation report of the bid commodity.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
According to the invention, the current commodity and the price of the bid product are obtained through the information monitoring unit, the price preprocessing model is established through the preliminary analysis unit, the abnormal change of the amplitude and the frequency of a price curve is analyzed for any commodity bid product, the price floating condition of the commodity bid product is integrated and obtained, the price comparison model is established through the deep comparison unit, the whole price floating condition of all the bid products is comprehensively analyzed, the price competitiveness of the bid product is further analyzed in depth, the risk degree of the current commodity and the bid product in price comparison competition is obtained, the price competition monitoring and early warning are carried out through the price prediction unit, the price competition risk degree of the current commodity is predicted, the commodity price is regulated and controlled in time, and the accurate, comprehensive and timely price monitoring management is realized, so that the market activity is maintained.
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FIG. 1 shows a schematic block diagram of the present invention;
Fig. 2 shows a schematic flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
1-2, the bid commodity price comparison system for electronic commerce transaction comprises an information monitoring unit, a preliminary analysis unit, a depth comparison unit and a price prediction unit, wherein the information monitoring unit, the preliminary analysis unit, the depth comparison unit and the price prediction unit are in signal connection;
The working steps are as follows:
S1: the information monitoring unit collects price information: setting an information acquisition period as Tc, and carrying out timing acquisition on price information, wherein the price information comprises commodity price and bid price, and the number of the acquired bid items is preset to be N0;
the price is the order price, namely the final transaction price after the discount coupon, the full discount and other movable promotion preferential means are removed from the bid price of the current commodity;
s2: the preliminary analysis unit establishes a price preprocessing model: firstly, establishing a dynamic curve change chart of commodity prices and N0 bid prices, and then carrying out price floating analysis on a single curve to obtain price floating evaluation coefficients corresponding to the curve;
The specific process for establishing the price preprocessing model is as follows:
A1: firstly, building a dynamic curve change graph of commodity prices and N0 bid prices:
marking the price of the current commodity as P0, marking any bid as I, marking the price of the bid as Pi, (wherein, I is more than 0 and less than or equal to N0, and I is a natural number);
constructing a dynamic curve graph S0 of the commodity price P0-information acquisition period Tc;
Constructing a dynamic curve graph Si of a bid price Pi-information acquisition period Tc;
a2: and then establishing a curve floating analysis model, and carrying out price floating analysis on a single curve:
A2-1: the curve input by the mark is Sj, floating analysis is carried out on the curve Sj, and a floating interval of the ordinate of the preset curve is [ J1, J2];
Extracting curve segments of which the ordinate exceeds a floating interval [ J1, J2] in a curve Sj, and marking the curve segments as abnormal segments of the floating curve, wherein the curve segments of which the ordinate is higher than J2 are marked as high-amplitude abnormal segments, and the curve segments of which the ordinate is lower than J1 are marked as low-amplitude abnormal segments;
The method comprises the steps of presetting the number of high-amplitude abnormal fragments and low-amplitude abnormal fragments to be n1 and n2 respectively, and analyzing the high-amplitude abnormal fragments and the low-amplitude abnormal fragments respectively:
A2-11: any one of the high-amplitude anomalous fragments is marked as Cg:
For the high-amplitude abnormal segment Cg, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kg1 and a right slope Kg2, wherein the left slope Kg1 is larger than 0, and the right slope Kg2 is smaller than 0;
all points with the slope of 0 are obtained, and marked as peaks Gu Biaodian of the high-amplitude abnormal segment Cg, the total number Mc0 of peaks Gu Biaodian of the high-amplitude abnormal segment Cg is obtained, any one of the peaks Gu Biaodian is marked as c0, and the high-amplitude peak point and the high-amplitude valley point are determined to be obtained:
the latter point of peak Gu Biaodian c0 is labeled as decision punctuation c1: if it is determined that the ordinate of the punctuation c1 is lower than the ordinate of the peak Gu Biaodian c0, then the peak Gu Biaodian c0 is marked as a high-amplitude peak; if it is determined that the ordinate of punctuation c1 is higher than the ordinate of peak Gu Biaodian c0, then this peak Gu Biaodian c0 is marked as a high-amplitude valley;
Any one of the high-amplitude peak points is marked as Fc0 (Xf 0, yf 0), the high-amplitude valley point adjacent to the high-amplitude peak point Fc0 is marked as Gc0 (Xg 0, yg 0), and the high-amplitude fluctuation slope BKc between the high-amplitude peak point Fc0 (Xf 0, yf 0) and the high-amplitude valley point Gc0 (Xg 0, yg 0) is obtained:
Because the total number of peaks Gu Biaodian is Mc0, high-amplitude fluctuation slopes are generated between adjacent high-amplitude peak points and high-amplitude valley points, and [ (Mc 0) -1] high-amplitude fluctuation slopes are sequentially obtained; if the high-amplitude fluctuation slope BKc is greater than 0, marking the high-amplitude fluctuation slope BKc as a high-amplitude fluctuation slope BKc +; if the high-amplitude fluctuation slope BKc is smaller than 0, marking the high-amplitude fluctuation slope BKc as a high-amplitude falling slope BKc -;
establishing a formula to obtain a high-amplitude anomaly coefficient GF of the high-amplitude anomaly segment Cg:
Wherein mu 1 is the conversion coefficient of the high-amplitude abnormal fragment Cg, mu 1 is larger than 0, the conversion coefficient is preset through a large number of experimental measurement and calculation, and m1 and m2 are the numbers of the high-amplitude rising slope BKc + and the high-amplitude falling slope BKc - respectively;
the higher the high-amplitude rising slope BKc + is, the higher the left slope Kg1 of the high-amplitude abnormal section Cg is, the higher the rise fluctuation of the high-amplitude abnormal section Cg is, the lower the high-amplitude falling slope BKc - is, the lower the right slope Kg2 of the high-amplitude abnormal section Cg is, the positive value is multiplied by the two, the higher the fall fluctuation of the high-amplitude abnormal section Cg is, and the higher the total number Mc0 of the peaks Gu Biaodian is, the higher the rise and fall change frequency of the high-amplitude abnormal section Cg is, and further the higher the high-amplitude abnormal fluctuation degree of the high-amplitude abnormal section Cg is comprehensively represented;
a2-12: any low-amplitude anomaly fragment is marked as Cd:
For the low-amplitude abnormal segment Cd, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kd1 and a right slope Kd2, wherein the left slope Kd1 is smaller than 0, and the right slope Kd2 is larger than 0;
And acquiring all points with the slope of 0, marking the points as peaks Gu Biaodian of the low-amplitude abnormal segment Cd, acquiring the total number Mc1 of peaks Gu Biaodian of the low-amplitude abnormal segment Cd, marking any one of the peaks Gu Biaodian as d0, and judging to acquire a low-amplitude peak point and a low-amplitude valley point:
The latter point of peak Gu Biaodian d0 is labeled as decision punctuation d1: if it is determined that the ordinate of the punctuation d1 is lower than the ordinate of the peak Gu Biaodian d0, then the peak Gu Biaodian d0 is marked as a low-amplitude peak; if it is determined that the ordinate of the punctuation d1 is higher than the ordinate of the peak Gu Biaodian d0, then the peak Gu Biaodian d0 is marked as a low-amplitude valley point;
marking any one low-amplitude peak point as Fd0, marking a low-amplitude valley point adjacent to the low-amplitude peak point Fd0 as Gd0, and obtaining a low-amplitude fluctuation slope BKd between the low-amplitude peak point Fd0 and the low-amplitude valley point Gd 0;
because the total number of peaks Gu Biaodian is Mc1, low-amplitude fluctuation slopes are generated between adjacent low-amplitude peak points and low-amplitude valley points, and [ (Mc 1) -1] low-amplitude fluctuation slopes are sequentially obtained; if the low amplitude fluctuation slope BKd is greater than 0, it is marked as a low amplitude rise slope BKd +; if the low amplitude fluctuation slope BKd is less than 0, it is marked as a low amplitude falling slope BKd -;
establishing a formula to obtain a low-amplitude anomaly coefficient DF of the low-amplitude anomaly fragment Cd:
Wherein, mu 2 is the conversion coefficient of the low-amplitude abnormal fragment Cd, mu 2 is more than 0, m3 and m4 are the numbers of low-amplitude falling slope BKd - and low-amplitude rising slope BKd + respectively;
When the lower the low-amplitude falling slope BKd - is, the lower the left slope Kd1 of the low-amplitude abnormal segment Cd is, the two are multiplied to be positive values and are higher, the higher the falling fluctuation of the low-amplitude abnormal segment Cd is, when the higher the low-amplitude rising slope BKd + is, the higher the right slope Kd2 of the low-amplitude abnormal segment Cd is, the higher the rising fluctuation of the low-amplitude abnormal segment Cd is, and when the total number Mc1 of the peaks Gu Biaodian is higher, the higher the increasing and decreasing change frequency of the low-amplitude abnormal segment Cd is, and the higher the low-amplitude abnormal fluctuation degree of the low-amplitude abnormal segment Cd is comprehensively represented;
A2-2: the method comprises the steps of marking high-amplitude peak points and low-amplitude peak points as abnormal peak points, marking high-amplitude valley points and low-amplitude valley points as abnormal valley points, setting time sections t of horizontal coordinates in a curve Sj, integrating and accumulating the abnormal peak points and the abnormal valley points in the time sections t to obtain total number Zs of abnormal points, setting a threshold Z0 of the total number of abnormal points, marking the total number Zs of abnormal points as high-frequency fluctuation segments when the total number of abnormal points exceeds the threshold Z0, extracting all high-frequency fluctuation segments in the curve Sj, and presetting n3 high-frequency fluctuation segments;
Analysis was performed for slope change of the high frequency fluctuation segment:
Marking any high-frequency fluctuation segment as Cp, firstly acquiring all points and slopes of the high-frequency fluctuation segment Cp, presetting that the high-frequency fluctuation segment Cp has n4 points, marking any point of the high-frequency fluctuation segment Cp as p, marking the adjacent point of the point p as q, marking the slope of the point p as Kp, marking the slope of the point q as Kq, and obtaining a slope change value Kpq between the point p and the point q: kpq= |Kp-Kq|, since the high-frequency fluctuation segment Cp has n4 points, slope change values are obtained between two adjacent points, and [ (n 4) -1] slope change values are sequentially obtained;
establishing a formula to obtain a high-frequency anomaly coefficient GP of a high-frequency fluctuation segment Cp:
wherein, mu 3 and mu 4 are respectively the weight factor coefficients of the total number of abnormal points Zs and the slope change value Kpq, and mu 3 and mu 4 are both larger than 0; when the total number Zs of abnormal points is higher and the slope change value Kpq is higher, it means that the degree of abnormal change of the high-frequency fluctuation segment Cp is higher;
A2-3: integrating n1 high-amplitude abnormal coefficients and n2 low-amplitude abnormal coefficients to generate amplitude abnormal coefficients, integrating high-frequency abnormal coefficients of n3 high-frequency fluctuation segments to generate frequency abnormal coefficients, and establishing a formula to obtain a price floating evaluation coefficient FD:
Wherein, alpha 1 and alpha 2 are respectively the weight factor coefficients of the amplitude anomaly coefficient and the frequency anomaly coefficient, and alpha 1 and alpha 2 are both larger than 0; when the high-amplitude anomaly coefficient, the low-amplitude anomaly coefficient and the high-frequency anomaly coefficient are higher, the price floating evaluation coefficient FD is represented to be higher;
A3: substituting the dynamic curve S0 of the commodity price P0 and the dynamic curves of the N0 bid prices into a curve floating analysis model respectively to obtain price floating evaluation coefficients corresponding to the curves:
marking the price floating evaluation coefficient of the dynamic curve S0 as FD0;
Marking the price floating evaluation coefficient of the dynamic curve Si as FDi;
S3: the depth comparison unit establishes a price comparison model: firstly, synthesizing the price floating evaluation coefficients of N0 competitive products to obtain the overall price evaluation index of the competitive products, and then comparing the price floating evaluation coefficient of the current commodity with the overall floating evaluation index of the competitive products to obtain the overall competition evaluation index;
S3-1: the specific process for acquiring the overall price evaluation index of the bid product is as follows:
Firstly, synthesizing price floating evaluation coefficients of N0 bid products, and establishing a formula to obtain the overall price floating index Zjp of the bid products:
wherein epsilon is a conversion coefficient of a price floating evaluation coefficient of the bid product, and epsilon is larger than 0; when the price floating evaluation coefficient of the bid is higher, the overall price floating index Zjp is higher, which means that the price floating comprehensive degree of N0 bids is higher and the bid price is unstable;
constructing a dynamic curve chart Sz of the overall price floating index Zjp-information acquisition period Tc of the bid product:
the ordinate average value of the dynamic curve Sz is obtained and marked as the bidding average price floating index Further, the maximum value Zmax of the overall price floating index Zjp of the bid is obtained through sequencing;
obtaining the current average price of the bid by averaging the current prices of N0 bids
Current average price of re-bidAverage price float indexAnd a maximum value Zmax of the overall price float index Zjp, and establishing a formula to obtain an overall price evaluation index Jjp of the bid:
Wherein the average price float index And the maximum value Zmax of the overall price floating index Zjp are combined to generate the price floating coefficient of the bid, and beta 1 and beta 2 are respectively the current average price of the bidThe weight factor coefficient of the price floating coefficient is larger than 0, and beta 1 and beta 2 are both larger than 0; when the current average price of the bid isThe lower the price floating coefficient is, the higher the overall price evaluation index Jjp of the bid is, which means that the price competitiveness of the bid is stronger;
S3-2: the specific process for obtaining the comprehensive competition evaluation index is as follows:
a formula is established to obtain the comprehensive competition evaluation index JZ of the current commodity through the price P0 of the current commodity, the price floating evaluation coefficient FD0 of the current commodity and the overall price evaluation index Jjp of the bid product:
The price P0 of the current commodity is combined with the price floating evaluation coefficient FD0 of the current commodity to generate a price evaluation coefficient DQ of the current commodity: ω1 and ω2 are respectively the proportionality coefficients of the price evaluation coefficient DQ of the current commodity and the overall price evaluation index Jjp of the bid, and both ω1 and ω2 are larger than 0;
If the price P0 of the current commodity and the price floating evaluation coefficient FD0 of the current commodity are higher, the price evaluation coefficient of the current commodity is lower, which means that the competitiveness of the current commodity is weaker; if the competitive power of the current commodity is weaker and the overall price evaluation index Jjp of the bid product is higher, the comprehensive competition evaluation index JZ of the current commodity is lower, which means that the lower the comprehensive competitive power of the current commodity is and the higher the risk degree in price comparison competition of the commodity bid product is;
S4: price competition monitoring and early warning of the price prediction unit: establishing a dynamic graph of the comprehensive competition evaluation index, performing curve anomaly monitoring analysis, judging the price competition risk degree of the current commodity, and generating an early warning prompt signal and a price comparison evaluation report of the competitive commodity, so that the commodity price is regulated and controlled in time, and accurate and comprehensive price monitoring management is realized;
The specific process of the dynamic curve anomaly monitoring analysis of the comprehensive competition assessment index is as follows:
with the information acquisition period Tc as an abscissa and the comprehensive competition evaluation index JZ of the current commodity as an ordinate, establishing a dynamic graph Sjz of the comprehensive competition evaluation index JZ-information acquisition period Tc of the current commodity, and carrying out anomaly monitoring analysis on the dynamic graph Sjz:
All points on the dynamic curve Sjz are acquired firstly, the N1 points of the dynamic curve Sjz are preset, any point is marked as W (Xw, yw), the point closest to the point W is marked as V (Xv, yv), and then the slope Kw of the point W is acquired: the slope of N1 points is determined and averaged to mark it as the average increment rate of the dynamic curve Sjz
Then obtaining the average index of comprehensive competition by obtaining the average value of the ordinate of N1 points of the dynamic curve Sjz
Further, the standard deviation is obtained, and the fluctuation coefficient sigma of the dynamic curve Sjz is obtained:
average rate increase through dynamic curve Sjz Average index of comprehensive competitionAnd combining the fluctuation coefficient sigma, and establishing a formula to obtain a price competition risk index FX:
Wherein Ym is the ordinate of the end point of the dynamic curve Sjz, if the fluctuation coefficient σ of the dynamic curve Sjz is higher, the dynamic curve Sjz is unstable, and the comprehensive competitiveness of the current commodity is unstable, the price competition risk index FX is higher, and the price competition risk of the current commodity is higher; while the end point ordinate Ym of the dynamic curve Sjz, the average increment rate And the integrated competition average indexThe higher the comprehensive competitiveness of the current commodity is, the lower the price competition risk index FX is, and the lower the price competition risk of the current commodity is;
Setting a risk interval of price competition risk index FX, dividing the competition risk degree of the current commodity, and generating a corresponding early warning prompt signal and a price comparison evaluation report of the bid commodity;
Setting a primary risk interval, a secondary risk interval and a tertiary risk interval, and correspondingly outputting a primary early warning prompt signal, a secondary early warning prompt signal and a tertiary early warning prompt signal respectively;
the early warning prompt signal is received through the visual background terminal to edit and display the commodity price competition risk degree:
When receiving the first-level early warning prompt signal, editing and displaying the text of commodity price competition risk degree high; when receiving the secondary early warning prompt signal, editing and displaying a text of commodity price competition risk degree intermediate; when receiving the three-level early warning prompt signal, editing and displaying a text of low commodity price competition risk;
The background staff timely regulates and controls the price of the current commodity according to the text prompt content, and when the commodity price competition risk degree is higher, the commodity price regulating and controlling range is higher, and the current commodity is reduced according to the regulating and controlling range; the price comparison evaluation report of the bid product shows all the analysis results, plays a certain reference role in regulating the degree range of the price, can provide more convenient price comparison results and commodity purchase recommendation for consumers by realizing timely comparison and monitoring of the price of the commodity and the bid product, and can timely regulate the price of the commodity by a merchant so as to ensure the competitiveness of the current commodity obtained by price comparison analysis of the current commodity and the bid product in the market environment, thereby maintaining market activity;
In summary, the present commodity and the price of the bid are obtained through the information monitoring unit, the price preprocessing model is established through the preliminary analysis unit, the abnormal change of the amplitude and the frequency of the price curve is analyzed for any commodity bid, the price floating condition of the commodity bid is integrated and obtained, the price comparison model is established through the deep comparison unit, the whole price floating condition of all the bids is comprehensively analyzed, the price competitiveness of the bid is further analyzed in depth, the risk degree of the present commodity and the bid in the price comparison competition is obtained, the price competitiveness monitoring and early warning are carried out through the price prediction unit, the price competition risk degree development trend of the present commodity is predicted, the commodity price is regulated and controlled in time, and the accurate, comprehensive and timely price monitoring management is realized, so that the market activity is maintained.
The interval and the threshold are set for the convenience of comparison, and the size of the threshold depends on the number of sample data and the number of cardinalities set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
The formulas are all formulas with dimensions removed and numerical calculation, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by a person skilled in the art according to the actual situation;
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (6)
1. A bid commodity price comparison system for electronic commerce transaction is characterized in that: the system comprises an information monitoring unit, a preliminary analysis unit, a depth comparison unit and a price prediction unit, wherein the information monitoring unit, the preliminary analysis unit, the depth comparison unit and the price prediction unit are in signal connection;
The information monitoring unit is used for collecting price information: setting an information acquisition period as Tc, and carrying out timing acquisition on price information, wherein the price information comprises commodity price and bid price, and the number of the acquired bid items is preset to be N0;
the preliminary analysis unit is used for establishing a price preprocessing model: firstly, establishing a dynamic curve change chart of commodity prices and N0 bid prices, and then carrying out price floating analysis on a single curve to obtain price floating evaluation coefficients corresponding to the curve;
The depth comparison unit is used for establishing a price comparison model: firstly, synthesizing the price floating evaluation coefficients of N0 competitive products to obtain the overall price evaluation index of the competitive products, and then comparing the price floating evaluation coefficient of the current commodity with the overall floating evaluation index of the competitive products to obtain the overall competition evaluation index;
The price prediction unit is used for price competitiveness monitoring and early warning: establishing a dynamic graph of the comprehensive competition evaluation index, performing curve anomaly monitoring analysis, judging the price competition risk degree of the current commodity, and generating an early warning prompt signal and a price comparison evaluation report of the competitive commodity so as to regulate and control the commodity price in time;
The specific process for establishing the price preprocessing model is as follows:
A1: firstly, building a dynamic curve change graph of commodity prices and N0 bid prices:
Marking the price of the current commodity as P0, marking any bid as I, and marking the price of the bid as Pi, wherein I is more than 0 and less than or equal to N0, and I is a natural number;
constructing a dynamic curve graph S0 of the commodity price P0-information acquisition period Tc;
Constructing a dynamic curve graph Si of a bid price Pi-information acquisition period Tc;
a2: and then establishing a curve floating analysis model, and carrying out price floating analysis on a single curve:
A2-1: marking an input curve as Sj, carrying out floating analysis on the curve Sj, presetting a floating interval of a ordinate of the curve as [ J1, J2], extracting a curve segment of the ordinate exceeding the floating interval [ J1, J2] in the curve Sj, and marking the curve segment as an abnormal segment of the floating curve, wherein the curve segment of the ordinate higher than J2 is marked as a high-amplitude abnormal segment, and the curve segment of the ordinate lower than J1 is marked as a low-amplitude abnormal segment;
The method comprises the steps of presetting the number of high-amplitude abnormal fragments and low-amplitude abnormal fragments to be n1 and n2 respectively, and respectively analyzing the high-amplitude abnormal fragments and the low-amplitude abnormal fragments to obtain a high-amplitude abnormal coefficient GF of a high-amplitude abnormal fragment Cg and a low-amplitude abnormal coefficient DF of a low-amplitude abnormal fragment Cd;
a2-2: integrating and marking high-amplitude peak points and low-amplitude peak points as abnormal peak points, integrating and marking high-amplitude valley points and low-amplitude valley points as abnormal valley points, setting a time section t of an abscissa in a curve Sj, integrating and accumulating the abnormal peak points and the abnormal valley points in the time section t to obtain the total number Zs of the abnormal points, setting a threshold Z0 of the total number of the abnormal points, marking the total number Zs of the abnormal points as high-frequency fluctuation segments when the total number of the abnormal points exceeds the threshold Z0, and extracting all the high-frequency fluctuation segments in the curve Sj;
N3 preset high-frequency fluctuation segments are provided, slope changes of the high-frequency fluctuation segments are analyzed, and high-frequency abnormal coefficients GP of the high-frequency fluctuation segments Cp are obtained;
A2-3: integrating n1 high-amplitude abnormal coefficients and n2 low-amplitude abnormal coefficients to generate amplitude abnormal coefficients, integrating high-frequency abnormal coefficients of n3 high-frequency fluctuation segments to generate frequency abnormal coefficients, and establishing a formula to obtain a price floating evaluation coefficient FD;
A3: substituting the dynamic curve S0 of the commodity price P0 and the dynamic curves of the N0 bid prices into a curve floating analysis model respectively to obtain price floating evaluation coefficients corresponding to the curves:
marking the price floating evaluation coefficient of the dynamic curve S0 as FD0;
the price-floating assessment coefficient of the dynamic curve Si is labeled FDi.
2. The bid commodity price contrast system for electronic commerce transactions according to claim 1, wherein: the specific process for analyzing the high-amplitude abnormal fragments and the low-amplitude abnormal fragments comprises the following steps:
a2-11: analyzing the high-amplitude abnormal fragments, and marking any high-amplitude abnormal fragment as Cg:
For the high-amplitude abnormal segment Cg, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kg1 and a right slope Kg2, wherein the left slope Kg1 is larger than 0, and the right slope Kg2 is smaller than 0;
all points with the slope of 0 are obtained, and marked as peaks Gu Biaodian of the high-amplitude abnormal segment Cg, the total number Mc0 of peaks Gu Biaodian of the high-amplitude abnormal segment Cg is obtained, any one of the peaks Gu Biaodian is marked as c0, and the high-amplitude peak point and the high-amplitude valley point are determined to be obtained:
Acquiring a high-amplitude fluctuation slope BKc through the slope between the adjacent high-amplitude peak point Fc0 and the high-amplitude valley point Gc0, wherein if the high-amplitude fluctuation slope BKc is larger than 0, the high-amplitude fluctuation slope BKc is marked as a high-amplitude rising slope BKc +; if the high-amplitude fluctuation slope BKc is smaller than 0, marking the high-amplitude fluctuation slope BKc as a high-amplitude falling slope BKc -;
the conversion coefficient of the high-amplitude abnormal segment Cg is given and a formula is established by combining the left slope Kg1, the right slope Kg2, the high-amplitude rising slope BKc +, the high-amplitude falling slope BKc - and the total number Mc0 of peak-valley punctuation points, so that the high-amplitude abnormal coefficient GF of the high-amplitude abnormal segment Cg is obtained;
A2-12: analyzing the low-amplitude abnormal fragments, and marking any low-amplitude abnormal fragment as Cd:
For the low-amplitude abnormal segment Cd, firstly acquiring slopes of two endpoints, and respectively marking the slopes as a left slope Kd1 and a right slope Kd2, wherein the left slope Kd1 is smaller than 0, and the right slope Kd2 is larger than 0;
And acquiring all points with the slope of 0, marking the points as peaks Gu Biaodian of the low-amplitude abnormal segment Cd, acquiring the total number Mc1 of peaks Gu Biaodian of the low-amplitude abnormal segment Cd, marking any one of the peaks Gu Biaodian as d0, and judging to acquire a low-amplitude peak point and a low-amplitude valley point:
Obtaining a low amplitude fluctuation slope BKd through the slope between the adjacent low amplitude peak point Fd0 and low amplitude valley point Gd0, wherein if the low amplitude fluctuation slope BKd is larger than 0, the low amplitude fluctuation slope is marked as a low amplitude rising slope BKd +; if the low amplitude fluctuation slope BKd is less than 0, it is marked as a low amplitude falling slope BKd -;
The conversion coefficient of the low-amplitude abnormal segment Cd is given and a formula is established by combining the left slope Kd1, the right slope Kd2, the low-amplitude rising slope BKd +, the low-amplitude falling slope BKd - and the total number Mc1 of peak-valley punctuations, so that the low-amplitude abnormal coefficient DF of the low-amplitude abnormal segment Cd is obtained.
3. The bid commodity price contrast system for electronic commerce transaction according to claim 2, wherein: the specific process for analyzing the high-frequency fluctuation segment is as follows:
Any one of the high frequency fluctuation segments is marked Cp: all points and slopes of a high-frequency fluctuation segment Cp are obtained, n4 points are preset in the high-frequency fluctuation segment Cp, any point of the high-frequency fluctuation segment Cp is marked as p, the point adjacent to the point p is marked as q, the slope of the point p is marked as Kp, the slope of the point q is marked as Kq, the slope change value Kpq between the point p and the point q is obtained, and as the high-frequency fluctuation segment Cp has n4 points, slope change values are obtained between the two adjacent points, [ (n 4) -1] slope change values are obtained in sequence;
And respectively endowing corresponding weight factor coefficients for the total number Zs of the abnormal points and [ (n 4) -1] slope change values, and establishing a formula to obtain a high-frequency abnormal coefficient GP of the high-frequency fluctuation segment Cp.
4. A bid commodity price contrast system for electronic commerce transactions according to claim 3, wherein: the specific process for acquiring the overall price evaluation index of the bid product is as follows:
Firstly, synthesizing price floating evaluation coefficients of N0 bid products, endowing conversion coefficients and establishing a formula to obtain an overall price floating index Zjp of the bid products;
reconstructing a dynamic curve Sz of the overall price floating index Zjp-information acquisition period Tc of the bid, and analyzing the curve Sz:
the ordinate average value of the dynamic curve Sz is obtained and marked as the bidding average price floating index
Further, the maximum value Zmax of the overall price floating index Zjp of the bid is obtained through sequencing, and the current average price of the bid is obtained by averaging the current prices of N0 bids
Current average price of re-bidAverage price float indexAnd the maximum value Zmax of the overall price float index Zjp, combine the average price float indexThe maximum value Zmax of the overall price floating index Zjp is combined to generate the price floating coefficient of the bid product;
for the current average price of the bid And respectively giving corresponding weight factor coefficients to the price floating coefficients, and establishing a formula to obtain the overall price evaluation index Jjp of the bid.
5. The bid commodity price contrast system for electronic commerce transactions according to claim 4, wherein: the specific process for obtaining the comprehensive competition evaluation index is as follows:
The price P0 of the current commodity is combined with the price floating evaluation coefficient FD0 of the current commodity by combining the price P0 of the current commodity, the price floating evaluation coefficient FD0 of the current commodity and the overall price evaluation index Jjp of the bid product, so that the price evaluation coefficient of the current commodity is generated;
and respectively endowing the price evaluation coefficient of the current commodity and the overall price evaluation index Jjp of the bid with corresponding proportion coefficients, and establishing a formula to obtain the comprehensive competition evaluation index JZ of the current commodity.
6. The bid commodity price contrast system for electronic commerce transactions according to claim 5, wherein: the specific process of the dynamic curve anomaly monitoring analysis of the comprehensive competition assessment index is as follows:
with the information acquisition period Tc as an abscissa and the comprehensive competition evaluation index JZ of the current commodity as an ordinate, establishing a dynamic graph Sjz of the comprehensive competition evaluation index JZ-information acquisition period Tc of the current commodity, and carrying out anomaly monitoring analysis on the dynamic graph Sjz:
All points on the dynamic curve Sjz are acquired firstly, N1 points are preset on the dynamic curve Sjz, any point is marked as W (Xw, yw), the point closest to the point W is marked as V (Xv, yv), and then the slope Kw of the point W is acquired; the slope of N1 points is determined and averaged to mark it as the average increment rate of the dynamic curve Sjz
Then obtaining the average index of comprehensive competition by obtaining the average value of the ordinate of N1 points of the dynamic curve SjzFurther solving standard deviation, and obtaining a fluctuation coefficient sigma of the dynamic curve Sjz;
the ordinate of the end point of the dynamic curve Sjz is obtained and compared with the average increment of the dynamic curve Sjz Average index of comprehensive competitionCombining the fluctuation coefficient sigma, and establishing a formula to obtain a price competition risk index FX;
setting a risk interval of price competition risk index FX, dividing the competition risk degree of the current commodity, and generating a corresponding early warning prompt signal and a price comparison evaluation report of the bid commodity.
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